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---
base_model: d0rj/rut5-base-summ
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: summary1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# summary1
This model is a fine-tuned version of [d0rj/rut5-base-summ](https://huggingface.co/d0rj/rut5-base-summ) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4999
- Rouge1: 0.1582
- Rouge2: 0.0671
- Rougel: 0.1582
- Rougelsum: 0.156
- Gen Len: 46.7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 90 | 2.4990 | 0.0834 | 0.0133 | 0.0858 | 0.0847 | 37.0 |
| No log | 2.0 | 180 | 2.4853 | 0.1484 | 0.0411 | 0.1431 | 0.1405 | 46.7 |
| No log | 3.0 | 270 | 2.4740 | 0.0753 | 0.0133 | 0.074 | 0.074 | 50.2 |
| No log | 4.0 | 360 | 2.4672 | 0.1468 | 0.0575 | 0.1472 | 0.14 | 53.9 |
| No log | 5.0 | 450 | 2.4647 | 0.1743 | 0.0824 | 0.1741 | 0.1694 | 46.1 |
| 1.6637 | 6.0 | 540 | 2.4651 | 0.1702 | 0.0436 | 0.1702 | 0.1658 | 48.3 |
| 1.6637 | 7.0 | 630 | 2.4683 | 0.1658 | 0.0545 | 0.1658 | 0.1606 | 48.7 |
| 1.6637 | 8.0 | 720 | 2.4716 | 0.1743 | 0.0545 | 0.1741 | 0.1694 | 46.2 |
| 1.6637 | 9.0 | 810 | 2.4758 | 0.1743 | 0.0545 | 0.1741 | 0.1694 | 48.2 |
| 1.6637 | 10.0 | 900 | 2.4780 | 0.1641 | 0.0678 | 0.1643 | 0.1593 | 50.0 |
| 1.6637 | 11.0 | 990 | 2.4819 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 47.4 |
| 1.3794 | 12.0 | 1080 | 2.4854 | 0.1621 | 0.0708 | 0.1621 | 0.1599 | 47.3 |
| 1.3794 | 13.0 | 1170 | 2.4875 | 0.1562 | 0.065 | 0.1576 | 0.1521 | 48.4 |
| 1.3794 | 14.0 | 1260 | 2.4886 | 0.1562 | 0.065 | 0.1576 | 0.1521 | 48.5 |
| 1.3794 | 15.0 | 1350 | 2.4908 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 47.3 |
| 1.3794 | 16.0 | 1440 | 2.4925 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 48.7 |
| 1.2935 | 17.0 | 1530 | 2.4942 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 47.3 |
| 1.2935 | 18.0 | 1620 | 2.4954 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 47.3 |
| 1.2935 | 19.0 | 1710 | 2.4971 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 47.5 |
| 1.2935 | 20.0 | 1800 | 2.4976 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 47.3 |
| 1.2935 | 21.0 | 1890 | 2.4981 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 46.9 |
| 1.2935 | 22.0 | 1980 | 2.4990 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 46.9 |
| 1.236 | 23.0 | 2070 | 2.4996 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 46.7 |
| 1.236 | 24.0 | 2160 | 2.4997 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 46.7 |
| 1.236 | 25.0 | 2250 | 2.4999 | 0.1582 | 0.0671 | 0.1582 | 0.156 | 46.7 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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